Machine learning based software fault prediction utilizing source code metrics

In the conventional techniques, it requires prior knowledge of faults or a special structure, which may not be realistic in practice while detecting the software faults. To deal with this problem, in this work, the proposed approach aims to predict the faults of the software utilizing the source code metrics. In addition, the purpose of this paper is to measure the capability of the software fault predictability in terms of accuracy, f-measure, precision, recall, Area Under ROC (Receiver Operating Characteristic) Curve (AUC). The study investigates the effect of the feature selection techniques for software fault prediction. As an experimental analysis, our proposed approach is validated from four publicly available datasets. The result predicted from Random Forest technique outperforms the other machine learning techniques in most of the cases. The effect of the feature selection techniques has increased the performance in few cases, however, in the maximum cases it is negligible or even the worse.